陀螺随机漂移误差模型建模方法研究

论述了自60年代以来的几种主要陀螺仪漂移误差模型建模的方法,介绍了各种方法的特点及建模公式,有助于惯导系统中陀螺漂移误差模型的建立．     

FINANCIAL VERSUS HUMAN RESOURCES IN THE GREEK–TURKISH ARMS RACE 10 YEARS ON: A FORECASTING INVESTIGATION USING ARTIFICIAL NEURAL NETWORKS

This paper looks into the Greek–Turkish arms race a decade after an earlier contribution to the issue that relied heavily on artificial neural networks. The time period between the two papers contributes to the reliability of the results derived, not just by increasing the number of observations, but also mainly by incorporating the progress made in the realm of artificial intelligence. The focus on the case of both countries unlike the paper mentioned above that dealt with just the Greek side provides ample room for comparative purposes regarding the determinants of defense expenditure on both sides. The results derived in terms of input significance estimation support the findings of the earlier research as indicated above, pointing to the leading role of the demographic preponderance of Turkey over Greece. The paper also points to the fact that 10 years later, Turkey continues to set the arms race rules against its rival by determining the defense expenditure of Greece, whereas the role of the latter in affecting the military spending of Turkey is non‐existent.     

神经网络应用

神经网络的应用是神经网络研究的三大主要领域之一。神经网络的应用与其所具备的能力是紧密相关的。本文中,我们从介绍神经网络的基本能力开始,概括地介绍了当前的神经网络主要应用领域。并从神经网络的能力出发,讨论了神经网络的潜在的应用领域。     

神经网络及其在军事上的应用

该文简要介绍了神经网络的特点、模型以及神经网络与传统计算机的不同之处。探讨了神经网络在军事上的应用,特别是在火控、指控系统中的应用前景。提出了我们研究神经网络的几点建议。     

物体识别不变性方法比较分析(Ⅲ)——神经网络方法

文献[1]和[2]中分别讨论了物体识别不变性方法——统计方法和解析方法。本文将讨论另一种方法——神经网络方法。     

神经计算机容错能力分析

容错能力是计算机(传统计算机或神经计算机)能力的一个极重要的方面。本文从研究传统计算机容错技术和人脑容错特性出发,着重研究了神经计算机的容错能力,并得出了对神经计算机设计极有价值的结论。     

神经网络距离-多普勒超分辨雷达成像

研究利用神经网络实现超分辨雷达成像。该方法首先根据Tuff的前后向线性预测(FBLP)方程用神经网络得到样本序列的AR模型参数,然后利用神经网络作线性预测外推,再用神经网络对外推后的数据作离散哈特来变换(DHT)。从哈特来变换可以方便地得到外推数据的离散傅里叶变换,从而得到距离-多普勒超分辨雷达像。利用微波暗室数据对这一方法进行了数字仿真,结果表明这种方法是可行的,即使在低信噪比下也有较好的性能。     

A novel facial emotion recognition scheme based on graph mining

Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. To reduce the number of generated sub-graphs, overlap ratio metric is utilized for this purpose. After encoding the final selected sub-graphs, binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification. Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the final system accuracy was 90.00%. The results show significant accuracy improvements (about 2%) by the proposed system in comparison to current published works in SAVEE database.     

基于神经网络的导弹系统辨识

详细地讨论了神经网络在导弹动态特性中的应用 ,利用神经网络的万能拟合能力进行了神经网络的训练和仿真 ,为导弹离散控制系统的设计提供了有效的工程数据 ,给导弹系统的系统辨识提供了一条新的方法和思路     

计算机辅助加工工件变形分析方法研究

为迅速、准确、合理地确定切削加工的工艺参数 ,将有限元方法、人工神经网络和遗传算法运用于计算机辅助加工工件变形分析中。通过合理的系统设计 ,实现了对工件切削加工变形量的准确预报和对加工参数的正确优化。实际生产证明 ,采用本方法优化确定加工参数 ,达到了预期的迅速、准确、合理的目标 ,有效地降低生产成本 ,提高生产率